Skill Needed for data engineers

 Primary Responsibilities & Qualification:

  • Lead Data Engineering activities by working closely with various teams/ members
  • Intensive software development experience under Agile development life cycle processes and tools
  • Strong understanding of data engineering concepts and best practices.
  • Proficiency in SQL and experience with data modeling techniques.
  • Familiarity with AWS services, particularly Redshift, S3, and Glue.
  • Knowledge of ETL (Extract, Transform, Load) processes and tools.
  • Excellent problem-solving and troubleshooting skills.
  • Strong communication skills to collaborate with cross-functional teams.

Data Warehouse Modeling:

  • Design and implement data models for the data warehouse.
  • Create and maintain data schemas, tables, and relationships.
  • Optimize data models for query performance and storage efficiency.
  • Ensure data integrity and enforce data quality standards.

Data Ingestion:

  • Develop and maintain data ingestion pipelines.
  • Extract data from various sources (databases, APIs, logs, etc.).
  • Transform and clean data as needed before loading it into Redshift.
  • Schedule and automate data ingestion processes.
  • Monitor and optimize data ingestion performance.

AWS Redshift:

  • Set up and configure Redshift clusters based on workload requirements.
  • Tune and optimize query performance through indexing and distribution strategies.
  • Monitor and manage Redshift performance, including workload management and query optimization.
  • Implement security measures and access controls for Redshift.
  • Ensure high availability and disaster recovery for Redshift clusters.

ETL (Extract, Transform, Load):

  • Develop ETL workflows using AWS Glue, Apache Spark, or other relevant tools.
  • Transform and enrich data during the ETL process to meet business requirements.
  • Handle schema evolution and data versioning in ETL pipelines.
  • Monitor ETL job performance and troubleshoot issues.
  • Implement data lineage and metadata management.

Data Governance and Compliance:

  • Implement data governance practices, including data lineage, data cataloging, and data documentation.
  • Ensure compliance with data privacy and security regulations (e.g., GDPR,).
  • Implement data retention policies and archiving strategies.

Automation and Monitoring:

  • Implement automation scripts and tools for managing data pipelines and workflows.
  • Set up monitoring and alerting for data pipeline failures and performance issues.
  • Conduct regular health checks and capacity planning for the data warehouse.

Documentation and Collaboration:

  • Maintain clear and up-to-date documentation for data processes, pipelines, and data models.
  • Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and deliver actionable insights.

Performance Tuning and Optimization:

  • Continuously optimize data warehouse performance through query tuning and resource management.
  • Implement Redshift best practices for workload management.
  • Identify and resolve bottlenecks in data pipelines and ETL processes.

Scalability and Cost Management:

  • Ensure the data warehouse infrastructure scales effectively to handle growing data volumes.
  • Monitor and manage costs associated with Redshift and other AWS services.
  • Implement cost-saving strategies without compromising performance.
  • Good knowledge on cyber security: penetration tests, DDOS attack prevention, TLS, PKI etc.
  • Application lifecycle management, DevOps, CI and CD
  • Experience in designing big data applications
  • This individual should be self-driven, highly motivated, and organized with strong analytical thinking and problem solving skills, and an ability to work on multiple projects and function in a team environment.


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